Load flow is an important tool used by power engineers for planning, to determine the best operation for a power system and exchange of power between utility companies. In order to have an efficient operating power system, it is necessary to determine which method is suitable and efficient for the system's load flow analysis. A power flow analysis method may take a long time and therefore prevent achieving an accurate result to a power flow solution because of continuous changes in power demand and generations. This paper presents analysis of the load flow problem in power system planning studies. The numerical methods: Gauss-Seidel, Newton-Raphson and Fast Decoupled methods were compared for a power flow analysis solution. Simulation is carried out using Matlab for test cases of IEEE 9-Bus, IEEE 30-Bus and IEEE 57-Bus system. The simulation results were compared for number of iteration, computational time, tolerance value and convergence. The compared results show that Newton-Raphson is the most reliable method because it has the least number of iteration and converges faster.

The Texas A&M University System was one of the first six Louis Stokes Alliance for Minority Participation (LSAMP) awardees. All current members of the Alliance are part of the Texas A&M University System. Many high impact practices (HIP) have been emphasized in the Alliance’s 30 years of programming with Diversity/Global Learning as a focus in the last 14 years. Diversity/Global Learning has been supported in two formats on the Alliance campuses, through traditional study abroad programming and a College of Engineering initiative. Data presented were derived from a number of sources, project evaluation information regarding student perspectives and outcomes, survey research conducted by an independent party, and institutional data and online platforms accessed to assess student outcomes. Triangulation was completed between data sets. Results indicate both forms of programming were efficacious for underrepresented and first-generation students. Outcomes reported were substantial increases in awareness of and interest in graduate school, increases in cultural learning, confidence in travel outside the United States, learning relevant to major, commitment to continuing involvement with research, interest in another similar experience, and willingness to consider employment outside the U.S. Participants reported statistically significant growth in personal, professional, and research skills. They persisted, participated in additional study abroad experiences, and graduated at higher rates than their institutional peers with approximately 90% of informants indicating intention to consider graduate school in the future, over 40% indicating intent to attend immediately following undergraduate study, and 39.4% of 2007–2014 participants enrolling in graduate school by the spring of 2021. Programming described is replicable at and likely to be efficacious for a wide variety of institutions of higher education.

This paper presents the design and implementation of a low-pass, high-pass and a hand-pass Finite Impulse Response (FIR) Filter using SPARTAN-6 Field Programmable Gate Array (FPGA) device. The filter performance is tested using Filter Design and Analysis (FDA) and FIR tools from Mathworks. The FDA Tool is used to define the filter order and coefficients, and the FIR tool is used for Simulink simulation. The FPGA implementation is carried out using Spartan-6 LX75T-3FGG676C for different filter specifications and simulated with the help of Xilinx ISE (Integrated Software Environment). System Generator ISE design suit 14.6i is used in synthesizing and co-simulation for FPGA filter output verification. Finally, comparison is done between the results obtained from the software simulations and those from FPGA using hardware co-simulation. The simulation waveforms and synthesis reports verify the parallel implementation of FPGA which proves its effectiveness in terms of speed, resource usage and power consumption.

Timely and accurate screening/testing is crucial to fighting COVID-19. Compared to commonly used reversetranscriptase polymerase chain reaction (RT-PCR), chest radiography imaging (X-ray) is also a reliable, practical and rapid method to diagnose and assess COVID-19. In this paper, two types of deep learning models, namely, Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet) have been designed and tested for accurate diagnosis of COVID-19 with chest X-ray images. Experimental results demonstrate the effectiveness of the proposed approach.

The world of engineering has changed significantly over the past ten years due to global competition along with fast moving technology. In order to meet the needs of industry, engineering graduates must hit the ground running. For this to occur industry and academia must compliment one another. In addition to presentations on theory, industrial experience should be included in the classroom. The student needs to make the connection between industry and theory early to attain a good understanding of the basic engineering principles. Research has shown that being exposed to the application of theory, results in a stronger understanding and mastering of the engineering subjects. Bringing industrial experience to the classroom will enhance the student's understanding of theory. It also allows an opportunity for dialogue in which the student can be made aware of the consequences of not mastering engineering concepts in industry, i.e. low performance ratings. Many employees who were not well prepared for industry end up switching careers. The value of the $50K or more spent per year by the state or parents for the degree is reduced. This paper discusses some of the key changes that have taken place in industry and the effect of these changes on the education of engineering students. The purpose of going to school and obtaining a four to five year engineering degree is to obtain a job or start a business to generate income and job satisfaction. In most cases the students are headed for an environment in which they know little about. The engineering curriculum today is based on theory and very little industrial interaction. Learning from the experience of engineers who have worked for many years is critical to narrowing the gap between industry and academia. Today's industry needs students who can achieve high levels of productivity within a short period of time.